Each sentence is assigned an importance score, indicating how well the sentence represents the main idea of the original documents. This can be achieved by first encoding the document(s) using a vector, and then computing the cosine similarity between the sentence vector and the document vector as the importance score.
Document encoding can be achieved by either aggregating the vectors of all the sentences in the document, or simply treating the document as a long sentence and applying the same sentence encoder.
More advanced methods also take into account inter-sentence dependencies and the discourse structure of documents [1]}, and introduce machine learning methods to estimate the scores on training data. We will present an example in detail in the next section.
